• 대한산업공학회지
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• 제19권4호
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• pp.115-123
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• 1993

This paper presents an alternative inspection method for counting items of a lot(or kit) in production lines or distribution centers. In this inspection, lots are weighed instead of counting all items of the lots in order to reduce the effort required for the 100% manual counting inspection. Inspection errors of this inspection procedure are analyzed and the impact of the variability of item weights on inspection errors are investigated. Two approaches, the cost assessment approach and the bicriterion decision making approach, are presented for the implementation of this inspection procedure.

• 한국사회복지학
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• 제63권1호
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• pp.85-111
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• 2011

다차원적 빈곤접근에 있어 빈자의 구분방식에는 복합지수방식, 합집합 및 교집합방식이 있는데 이들 방식들은 타당하지 않은 문제들이 있었다. Alkire와 Foster는 이 문제를 해소하는 방법으로 합집합과 교집합의 중간 형태로서 결핍차원들의 개수를 경계선으로 이용하는 차원계수방식을 이론화하였다. 차원계수방식에 의해 우리나라의 다차원적 빈곤을 측정한 결과, 3개 결핍차원을 정책적 차원빈곤선으로 하는 경우 다차원적 빈곤율은 20% 수준으로서 10명 중 2명이 다차원적으로 빈곤하였다. 다차원적 빈곤율이 높은 것은 자산, 소득, 사회보장, 건강 등 여러 차원으로 결핍의 폭이 넓은데 기인하였다. 여성, 한 부모, 노인, 비경제활동인구 등 취약계층일수록 다차원 빈곤의 폭이 넓고 가중되고 있었다. 연구결과 현행 기초생활보장제도가 탈 빈곤유도와 기초생활보장이라는 두 가지 정책목표를 각각 효과적으로 달성하기 위해서는 근로능력 유무에 따라 수급자선정과 지원체제를 이원화하고 차원계수방식을 적용하는 것이 유용하다고 본다.

• 한국조사연구학회지:조사연구
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• 제12권1호
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• pp.141-152
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• 2011

지상파 방송 3사에서 선거 개표방송을 할 때 사용하는 현재의 개표 집계방식은 각 개표소에서 집계된 개표결과를 단순 합산하여 발표하는 방식이다. 그런데 이 방식은 투표소별 개표 진도의 차이를 무시하는 방식이어서 불필요한 혼선을 초래할 여지가 있다. 방송사 입장에서는 이미 출구조사를 통해 얻은 지역별 데이터가 있는데도 불구하고 이 정보를 오후 6시 예측결과를 발표할 때에만 사용할 뿐이고, 이후 개표가 진행되는 동안에는 전혀 이용하지 않은 채 개표결과만을 단순 집계하여 발표한다. 본 논문에서는 베이지안(Bayesian) 기법을 도입하여 출구조사 자료와 개표결과를 통합하여 발표하는 방법을 제시하고자 한다. 이 방법을 사용함으로써 투표소별 개표 진도의 차이에서 생기는 혼선을 피할 수 있을 것으로 기대한다.

• Environmental Engineering Research
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• 제24권3호
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• pp.397-403
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• 2019

Microcystis sp. is one of the most common harmful cyanobacteria that release toxic substances. Counting algal cells is often used for effective control of harmful algal blooms. However, Microcystis sp. is commonly observed as a colony, so counting individual cells is challenging, as it requires significant time and labor. It is urgent to develop an accurate, simple, and rapid method for counting algal cells for regulatory purposes, estimating the status of blooms, and practicing proper management of water resources. The flow cytometer and microscope (FlowCAM), which is a dynamic imaging particle analyzer, can provide a promising alternative for rapid and simple cell counting. However, there is no accurate method for counting individual cells within a Microcystis colony. Furthermore, cell counting based on two-dimensional images may yield inaccurate results and underestimate the number of algal cells in a colony. In this study, a three-dimensional cell counting approach using a novel model algorithm was developed for counting individual cells in a Microcystis colony using a FlowCAM. The developed model algorithm showed satisfactory performance for Microcystis sp. cell counting in water samples collected from two rivers, and can be used for algal management in fresh water systems.

• 정보처리학회논문지B
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• 제12B권2호
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• pp.209-214
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• 2005

단백질 상호작용 데이터는 현 생물정보학에서 기능이 알려져 있지 않은 단백질의 기능 예측에 높은 신뢰성이 있는 프로티오믹스의 계산 모델에 이용되고 있다. 단백질 기능 예측 관련 연구로는 guilt-by-association 개념을 바탕으로 대규모의 단순 2차원 단백질-단백질 상호작용 맵을 이용하고 있다. 본 논문에서는 단백질-단백질 상호작용 데이터를 이용한 그래프 기반 기능 예측 방법인 neighbor-counting, $\chi^2$-통계치 예측 모델을 살펴보고 대량의 상호작용 데이터로부터 빠른 기능예측에 효과적인 알고리즘을 제안한다. 제안하는 알고리즘은 단백질 상호작용 맵, 서열 유사성 및 경험적 전문가 지식을 이용하는 그래프 기반 모델이다. 제안된 알고리즘은 Yeast 단백질의 기능 예측을 수행하였으며, neighbor-counting, $\chi^2$-통계치 모델의 실험 결과와 비교되었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권12호
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• pp.4866-4888
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• 2020

One major and time-consuming task in fish production is obtaining an accurate estimate of the number of fish produced. In most Nigerian farms, fish counting is performed manually. Digital image processing (DIP) is an inexpensive solution, but its accuracy is affected by noise, overlapping fish, and interfering objects. This study developed a catfish recognition and counting algorithm that introduces detection before counting and consists of six steps: image acquisition, pre-processing, segmentation, feature extraction, recognition, and counting. Images were acquired and pre-processed. The segmentation was performed by applying three methods: image binarization using Otsu thresholding, morphological operations using fill hole, dilation, and opening operations, and boundary segmentation using edge detection. The boundary features were extracted using a chain code algorithm and Fourier descriptors (CH-FD), which were used to train an artificial neural network (ANN) to perform the recognition. The new counting approach, based on the geometry of the fish, was applied to determine the number of fish and was found to be suitable for counting fish of any size and handling overlap. The accuracies of the segmentation algorithm, boundary pixel and Fourier descriptors (BD-FD), and the proposed CH-FD method were 90.34%, 96.6%, and 100% respectively. The proposed counting algorithm demonstrated 100% accuracy.

• Nuclear Engineering and Technology
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• 제27권5호
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• pp.784-791
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• 1995

방사선계측에서는 방사선검출기(detector)로부터 나오는 pulse를 처리하는 데 있어서 pulse의 counting 손실이 발생한다. 이 손실을 최소화하거나 보상하기 위한 여러가지 방법들이 제시되어 왔으나, 아직도 절대적인 해답이 확립되지 않은 실정이다. 본 연구에서는 기 제시된 보상알고리즘들을 그 기능을 구현하는 전자회로와 함께 기술하고 특징을 분석하였다. 또한 본 연구를 통해 pulse의 counting손실을 보상하는 한가지 알고리즘 개선방향을 제시하였다.

• East Asian mathematical journal
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• 제39권3호
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• pp.331-347
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• 2023

We present an identity on moments of a compound random variable by using an auxiliary counting random variable. Based on this identity, we develop a new recurrence formula for obtaining the raw and central moments of any order for a given compound random variable.

• 대한영양사협회학술지
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• 제5권1호
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• pp.64-73
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• 1999

Carbohydrate(CHO) counting is a meal planning approach used with diabetic patients that focuses on carbohydrate as the primary nutrient affecting post-prandial glycemic response. However, it has not been used in meal management of diabetic patients in Korea. CHO counting can be used by clients with type 1 and 2 diabetes. The purpose of the study was to determine the barriers to utilize the CHO counting when three levels of CHO counting were educated to type 2 diabetic patients who started continuous subcutaneous insulin infusion (CSⅡ) therapy by nutrition lectures and counseling. And the CHO-to-insulin ratios were determined for the individual patients who followed the carbohydrate counting as a meal management, and the factors to influence the CHO-to-insulin ratios were selected through the stepwise regression analysis. Twenty- four subjects were received three lectures, and one or two nutritional counseling for a month. The average age of the subjects was 50.7 years, and the duration of diabetes was 9.4 years. Their body mass index (BMI) was 21.5 kg/$m^2$. The difficulties of using CHO counting were 1) confusing the CHO exchange system to diabetic food exchange system, 2) lack of basic nutrition and not distinguishing nutrients such as CHO, fat and calorie, and 3) lack of motivation to make effort to count and record the amount of carbohydrates eaten. Nutritional counseling replenished the nutrition education and made patients practice CHO counting. Average CHO-to-insulin ratios at breakfast, lunch and dinner were 4.1$\pm$3.3, 2.9$\pm$2.6 and 2.9$\pm$3.0units/23g of CHO, respectively. CHO-to-insulin ratios were influenced by gender, age, BMI, post-prandial blood glucose levels and post-prandial c-peptide levels. The effective education and nutritional counseling of CHO counting can make CHO counting applicable to type 2 diabetic patients as meal management for improving glycemic control with less hypoglycemic episode.

• 한국멀티미디어학회논문지
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• 제22권3호
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• pp.335-348
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• 2019

Cell segmentation and counting represent one of the most important tasks required in order to provide an exhaustive understanding of biological images. Conventional features suffer the lack of spatial consistency by causing the joining of the cells and, thus, complicating the cell counting task. We propose, in this work, a cascade of networks that take as inputs different versions of the original image. After constructing a Gaussian pyramid representation of the microscopy data, the inputs of different size and spatial resolution are given to a cascade of deep convolutional autoencoders whose task is to reconstruct the segmentation mask. The coarse masks obtained from the different networks are summed up in order to provide the final mask. The principal and main contribution of this work is to propose a novel method for the cell counting. Unlike the majority of the methods that use the obtained segmentation mask as the prior information for counting, we propose to utilize the hidden latent representations, often called the high-level features, as the inputs of a neural network based regressor. While the segmentation part of our method performs as good as the conventional deep learning methods, the proposed cell counting approach outperforms the state-of-the-art methods.